Service providers and device manufacturers (e.g., wireless, cellular, etc.) are continually challenged to deliver value and convenience to consumers by, for example, providing compelling network services. One such compelling network service is the service of providing recommendations to users regarding recommended content. Certain recommendation systems, such as collaborative recommendation models, may base recommendations for a user on other users or other items that are associated with the user based on various activities. The collection of information regarding the users, the items, and the activities allows for recommendation service providers to collect a large amount of information to process and subsequently use to generate the recommendations. However, there are scalability issues that result from such recommendation models based on the extensive computational problems required to handle all of the information, particularly the new information as additional activities associated with the users and items are collected. Other issues with recommendation models exist, such as providing recommendations that a user may more confidently rely on based on the source of the recommendation. Accordingly, service providers and device manufacturers face significant technical challenges in handling the scalability of recommendation models while maintaining accurate recommendations that a user may confidently rely on.